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An optimization approach to partitional data clustering

Author

Listed:
  • J Kim

    (Korea Small Business Institute(KOSBI))

  • J Yang

    (Chonbuk National University)

  • S Ólafsson

    (Iowa State University)

Abstract

Scalability of clustering algorithms is a critical issue facing the data mining community. One method to handle this issue is to use only a subset of all instances. This paper develops an optimization-based approach to the partitional clustering problem using an algorithm specifically designed for noisy performance, which is a problem that arises when using a subset of instances. Numerical results show that computation time can be dramatically reduced by using a partial set of instances without sacrificing solution quality. In addition, these results are more persuasive as the size of the problem is larger.

Suggested Citation

  • J Kim & J Yang & S Ólafsson, 2009. "An optimization approach to partitional data clustering," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(8), pages 1069-1084, August.
    • Handle: RePEc:pal:jorsoc:v:60:y:2009:i:8:d:10.1057_jors.2008.195
    DOI: 10.1057/jors.2008.195
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    References listed on IDEAS

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    1. Ja-Shen Chen & Russell K H Ching & Yi-Shen Lin, 2004. "An extended study of the K-means algorithm for data clustering and its applications," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(9), pages 976-987, September.
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    3. Basu, Amit, 1998. "Perspectives on operations research in data and knowledge management," European Journal of Operational Research, Elsevier, vol. 111(1), pages 1-14, November.
    4. Leyuan Shi & Sigurdur Ólafsson, 2000. "Nested Partitions Method for Global Optimization," Operations Research, INFORMS, vol. 48(3), pages 390-407, June.
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    Cited by:

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    2. Amin, Gholam R. & Emrouznejad, Ali & Rezaei, S., 2011. "Some clarifications on the DEA clustering approach," European Journal of Operational Research, Elsevier, vol. 215(2), pages 498-501, December.

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